Annals of Neurology

Background Childhood neurodegenerative disorders are usually rare, genetic, and life-limiting. Whilst targeted approaches present huge potential, significant hurdles include disease rarity, geographical dispersion of patients, funding, clinical trial design, and execution. Crucially, the paucity of robust biomarkers and objective measures of disease progression hampers evaluation of efficacy, drug development and regulatory approval. To address this paradigm, we developed a Multimodal Multiomics Machine Learning (MMM) framework, integrating large-scale, multi-source patient datasets to generate quantitative metrics for disease stratification and longitudinal tracking. We applied MMM to PLA2G6-associated neurodegeneration (PLAN), an ultra-rare condition currently lacking validated biomarkers, where precision gene therapy approaches are at an advanced preclinical stage. Methods A large, single time-point international natural history study (n = 310) was conducted alongside development of a disease-specific rating scale (CoPLAN-DRS), prospective longitudinal neuroimaging, and multiomic biomarker discovery. Machine learning methods were applied to the integrated dataset. Results Kaplan-Meier analyses enabled estimates for survival and time to loss of ambulation. Multiple clinical, radiological, and biofluid biomarkers were identified, clearly correlating with disease progression. The CoPLAN-DRS and brain MRI Quantitative Susceptibility Mapping showed strong positive correlation with age (rho = 0.69, 0.96 respectively). Nicastrin, a critical structural component of the gamma-secretase complex in Amyloid Precursor Protein (APP) processing, was identified as a novel biomarker. Neurofilament light levels showed strong negative correlation with disease progression (rho = -0.74). The complex multi-dimensional dataset was distilled into a simplified, clinically intuitive Digital Disease Dashboard (DDD), enabling real-time visualisation of disease severity. Conclusions Our study highlights the clinical utility of MMM in integrating multi-dimensional data from rare disease cohorts, delivering an unbiased, data-driven, optimised biomarker set. Condensing this into the DDD provides a pragmatically useful tool for clinicians, facilitating longitudinal tracking of disease. The MMM and DDD have accelerated clinical-trial readiness for PLAN, and potentially applicable to a broad range of neurogenetic disorders.

Background: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is hypothesized to improve motor symptoms in Parkinson's disease (PD) by suppressing pathologically elevated beta activity and promoting "prokinetic" gamma activity in the cortico-basal ganglia-thalamo-cortical loop. Advances in bidirectional DBS devices have revealed that stimulation can modify gamma oscillations via subharmonic entrainment, though entrainment's therapeutic role remains unclear. Objectives: To identify stimulation parameters that entrain motor cortical and STN gamma oscillations in PD at rest and during movement, and examine their association with motor function. Methods: Sensorimotor cortex and STN field potentials were collected using a bidirectional DBS system in four subjects with PD over a range of stimulation amplitudes and frequencies. Entrainment amplitude at half the stimulation frequency was quantified at rest and during a finger-tapping task in the ON-medication state. The presence or absence of entrainment was studied as a physiomarker of motor symptom severity. Results: The amplitude of stimulation-entrained gamma oscillations was non-linearly related to stimulation intensity and frequency and varied by stimulation contact choice. Entrainment amplitude was highest in precentral gyrus and increased with movement. In the ON-medication state, precentral gyrus gamma entrainment was associated with reduced bradykinesia, dyskinesia, and dystonia. Subthalamic gamma entrainment predicted improved dystonia but was a less significant marker for motor benefit than cortical entrainment. Conclusions: Stimulation-entrained gamma oscillations in the motor network are a physiomarker for optimal DBS response in PD, and could have a role in physiology-guided DBS programming, complementing existing strategies based on suppression of basal ganglia beta activity.

Blood-based (BB) biomarker investigations in Parkinson disease (PD) and in mild traumatic brain injury (mTBI) have substantially grown over the past decade. High risks for PD in young post-mTBI veterans have been inferred from medical record data using actuarial modeling. However, potential utility of BB biomarkers to quantify risks vs. no risk for PD in young post-mTBI veterans has not been established. Previously we reported post-mTBI veterans performed significantly below the standardized normative scores for their age and education level on specific domains of executive functioning, on par with senior aged individuals with early-stage PD. Here, we examined serum brain-derived neurotropic factor (BDNF), ubiquitin C-terminal hydrolase-L1 (UCH-L1), glial fibrillary acidic protein (GFAP), and S100 calcium-binding protein {beta} (S100B) in association with executive functioning outcomes in search of a bio-cognitive model suitable to differentiate risk from no risk for prodromal PD. A reference range of bio-cognitive cut-points were derived from Area Under the Curve (AUC) sensitivity and specificity methods. Our data revealed two bio-cognitive signatures with reference range cut-points when GFAP was paired with cognitive flexibility / attention scores, and when S100B was paired with categorical / semantic verbal memory scores. Both bio-cognitive signatures revealed prodromal PD risk vs.no-risk parameters that remained relevant for differentiating young veterans who had encountered a past mTBI and those who had not experienced a mTBI. Subjects with early-stage PD who had withstood a mTBI up to 10- to 40-years earlier were also differentiated from those who had no mTBI history. These results indicate the predictive utility of expanding the biomarker field to include reference ranges, cut-points, and specific cognitive domains to estimate risks for PD in a clinic setting. These preliminary data also add value in establishing a quantifiable bio-cognitive risk signature to identify prodromal PD risks in young adults prior to obvious cognitive and motor decline. While encouraging, these data require further follow-up with a larger sample size in a longitudinal design to validate these findings.

Background and Objectives To investigate the associations of blood-based tau biomarkers with clinical, electrophysiologic and prognostic measures in amyotrophic lateral sclerosis (ALS), and to determine whether they reflect distinct disease-related processes. Methods We studied 119 patients with ALS from a longitudinal observational cohort. Plasma and serum p-tau181, p-tau217, p-tau231, brain-derived tau (BD-tau), NfL and GFAP were measured using Lumipulse and Simoa assays. Associations with demographic variables, disease severity (ALSFRS-R and slow vital capacity), lower motor neuron (LMN), muscle involvement (creatine kinase [CK] and high-sensitivity cardiac troponin T [hs-cTnT]), disease progression and survival were assessed using multivariable models. Results Tau-related biomarkers, specifically p-tau217 and BD-tau, were associated with greater cross-sectional disease severity, reflected by lower ALSFRS-R scores. Plasma and serum p-tau181, p-tau217, p-tau231, and BD-tau were associated with higher CK and hs-cTnT, whereas p-tau181 and p-tau231 were also associated with greater LMN involvement. In contrast, NfL and GFAP were not associated with muscle or LMN involvement. Across analytical platforms, plasma and serum NfL were associated with faster ALSFRS-R decline and shorter survival. NfL was the only biomarker independently associated with both disease progression and survival. Discussion Blood biomarkers capture distinct dimensions of ALS. Tau-related biomarkers are associated with cross-sectional disease severity, LMN involvement and muscle injury, whereas NfL primarily reflects disease progression and survival. These findings support the complementary use of tau-related biomarkers and NfL for ALS phenotypic characterization and prognosis assessment.

Background: Diabetes mellitus elevates Parkinson's disease (PD) risk, via hypothesized cerebrovascular mediation. Whether the diabetes/prediabetes vascular-risk phenotype concentrates in cardiometabolic risk or macrovascular events across prodromal and clinically diagnosed PD remains unresolved. Objectives: To quantify the vascular-risk burden associated with diabetes/prediabetes across the PPMI diagnostic cohorts to test whether this association differs by cohort. Methods: Cross-sectional analysis of 413 PPMI participants (76 healthy controls, 145 prodromal PD, 192 clinically diagnosed PD) examined diabetes/prediabetes (n = 73) and seven vascular risk factors. The Vascular Burden Score (0 to 7) was a priori partitioned into microvascular and macrovascular sub-scores. Modified Poisson regression estimated adjusted prevalence ratios (aPR), adjusted for age, sex, and body mass index. A cohort-by-diabetes interaction tested cross-cohort consistency. Sensitivity analyses incorporated nigral diffusion tensor imaging (PD-risk biomarker) and FreeSurfer white matter hypointensity volume (cerebrovascular marker). Results: Diabetes/prediabetes elevated Vascular Burden Score ({beta} = 0.53, 95% CI 0.29 to 0.77, p < 0.001) versus non-diabetic participants, with a non-significant cohort-by-diabetes interaction (F = 0.29, p = 0.747). Three microvascular factors survived false discovery rate correction: obesity (aPR 2.28), hypertension (aPR 1.60), and hyperlipidemia (aPR 1.45). Macrovascular events showed no diabetic amplification ({beta} = -0.06, p = 0.25). In the imaging-phenotyped subset, Vascular Burden Score components contributed classifier variance distinct from nigral microstructure. Conclusions: Diabetes/prediabetes operationalize a microvascular cluster stable across prodromal and idiopathic PD. Cardiometabolic phenotyping may complement established PD-risk biomarkers (dopamine transporter SPECT, nigral diffusion), pending longitudinal validation linking vascular phenotype to dopaminergic markers.

Background: Serum neurofilament light chain (NfL) indexes axonal injury and glial fibrillary acidic protein (GFAP) astrocytic pathology in multiple sclerosis (MS). GFAP rises disproportionately as relapsing-remitting MS (RRMS) shifts to progressive forms on research-grade SIMOA. The commercial Roche Elecsys ECLIA platform reads six-fold lower and is undescribed across subtypes. Objective: To describe both markers by MS subtype on ECLIA. Methods: Retrospective single-center analysis of 603 MS patients (2022-2026). NfL and GFAP were measured by LabCorp Roche Elecsys ECLIA; subtype came from ICD-10 codes and notes. We examined both markers by subtype, their correlation, and NfL against gadolinium-enhancing (Gd+) MRI lesions. Results: Median NfL was 1.32 pg/mL (IQR 1.01-1.91). Both rose with stage, steeper for GFAP: NfL 1.18 (RRMS), 1.54 (SPMS, p<0.001), 1.78 (PPMS, p=0.001); GFAP 41.90, 63.80 (p<0.0001), 75.75 (p=0.08, n=6). SPMS and PPMS GFAP did not differ (p=0.83). The markers correlated moderately (r=0.569). Of 34 Gd+ encounters with NfL within 30 days, 3 (9%) were elevated. Conclusion: On ECLIA, both markers rose with MS stage, GFAP more steeply, and both progressive subtypes exceeded RRMS. NfL rarely flagged a recent Gd+ lesion, consistent with its delayed kinetics. The two index distinct processes and reproduce on an orderable assay a profile once confined to research-grade SIMOA.

Early acquired brain injury is a major risk factor for infantile epileptic spasms syndrome (IESS), which may impair cognitive development, especially if diagnosis and treatment are delayed. However, individual-level prediction of which infants will develop IESS is currently not possible. We assessed whether high-frequency oscillations (HFOs) in scalp EEG or recurrent interictal epileptiform discharges (IED) during the first months of life could predict forthcoming IESS. Our population-based cohort included 36 infants with cortical injury due to infarction, haemorrhage, infection or trauma involving a large cortical area ([&ge;] anterior/posterior cerebral artery territory or [&ge;] half of the middle cerebral artery territory), or hypoxic-ischaemic encephalopathy with cortical and deep grey matter involvement. The infants underwent repeated EEGs during the first year of life until 12 months of age or until IESS diagnosis. HFOs during sleep were scored both visually and automatically, whereas IEDs were assessed visually only. We tested whether HFO rate increased during the first year of life using a mixed-effects model with within- and between-subject random effects. Using only EEGs recorded prior to IESS diagnosis, we evaluated whether HFO rate or recurrent IEDs could predict IESS development by training a ridge-regularized logistic regression model with exhaustive leave-2-subjects-out cross-validation. Eleven infants (31%) developed IESS. HFO rate increased with age in both groups but more steeply in the IESS group [within person slope {beta} = 2.43 (IESS) vs. 0.06 (no-IESS) units/month, P < 0.001]. The logistic regression model showed that both HFO rate [AUC 0.801 (95% CI 0.668, 0.936)] and recurrent IEDs [AUC 0.826 (95% CI 0.720, 0.932)] were able to predict forthcoming IESS. However, in a multivariable model, only recurrent IEDs remained independently associated with IESS, and HFO rate did not add predictive value. The marked increase in HFO rate toward IESS diagnosis supports their role as a biomarker of epileptogenesis. During the first months of life, HFOs and recurrent IEDs performed equally well in predicting subsequent IESS. However, IEDs are easier to apply to clinical practice.

Responsive neurostimulation (RNS) is an implanted device that delivers direct brain stimulation for drug-resistant focal epilepsy. Individual responses are highly variable, and no validated framework exists to predict outcome or guide lead placement before implantation. We hypothesized that this variability is partly explained by lead placement in relation to patterns of functional connectivity in brain networks. Fourty-nine patients with drug-resistant focal epilepsy who underwent pre-implantation intracranial EEG (iEEG) and RNS implantation across three independent epilepsy centers were retrospectively studied. We developed a composite functional connectivity score, based on simple Spearman correlation, combining the standard deviation and kurtosis of interictal iEEG connectivity distributions to predict the response outcome in a training cohort (HUP, n=18) and validated in two independent cohorts (NYU, n=17; UCSF, n=14). We accounted for a spatial mismatch between iEEG and RNS electrodes with a distance-based correction. The score was extended to generate patient-specific 3D maps of predicted RNS efficacy across 200 simulated, or virtual RNS, lead configurations. Accuracy of the score in predicting clinical outcome was 72% at the group level, 61% at the individual patient level, and, after distance-based optimization, 100% in patients with RNS electrodes placed close to location of iEEG electrodes. Applied to the validation cohort, the same score reached 68% accuracy (71% balanced accuracy, 55% sensitivity, 88% specificity). The spatial combination of the scores at different SEEG contacts localization gives a spatial score for each patient. Responders showed significantly higher spatial scores than non-responders, supporting that actual RNS lead placement in responders was located in map-identified favorable regions. Interictal iEEG functional connectivity predicts individual RNS response across independent epilepsy centers, and patient-specific 3D maps derived from this biomarker could prospectively guide lead implantation toward favorable network regions, opening a promising avenue toward network-informed RNS surgical planning.

Estimating Parkinson's disease (PD) risk years before diagnosis remains an unmet need. We applied a validated machine learning classifier for REM sleep behavior disorder (RBD) detection to 7-day wrist accelerometry data in 87,975 UK Biobank participants followed for 10 years. Participants in the highest RBD risk stratum (>99th percentile) had an approximately fivefold increased hazard of incident PD compared with the lowest-risk group (0-90th percentile), with a dose-dependent relationship across the full score distribution. Among non-converters, higher RBD risk was associated with baseline cognitive deficits and longitudinal enrichment of autonomic and psychiatric prodromal features. The association with incident PD remained independent of PD polygenic risk score, while RBD score and genetic risk were synergistic. The combined high-risk group achieved a positive likelihood ratio of 7.91, approximately threefold higher than questionnaire-based RBD screening. These findings support wrist accelerometry as a scalable approach for prodromal PD risk enrichment in population screening.

LRRK2 is implicated in Parkinson's disease (PD) microbiome-gut-brain axis. We compared plasma lipopolysaccharide-binding protein (LBP) and soluble CD14 (sCD14), markers of gut permeability and endotoxin exposure, in PD patients with/without LRRK2 p.G2385R and/or p.R1628P, and controls, and examined their associations with systemic inflammation and clinical severity. Neither marker differed between groups. Across PD patients, LBP correlated with higher IL-6, TNF- and worse motor function, while sCD14 correlated with higher IL-6, CCL5 and worse constipation. These findings highlight the clinical relevance of endotoxin-related immune signaling in PD, without LRRK2 risk variant-specific associations and identify LBP as an emerging marker of inflammatory burden.

Background: Parkinsons disease (PD) has a prolonged prodromal phase, but minimally invasive molecular biomarkers distinguishing manifest PD from prodromal synucleinopathy remain insufficiently characterized. Isolated REM sleep behavior disorder (iRBD) represents a high-risk prodromal condition and provides an opportunity to investigate early blood-based transcriptional alterations. Objective: To identify peripheral blood transcriptomic signatures distinguishing healthy controls (HC), individuals with iRBD, and patients with PD, and to explore whether longitudinal iRBD samples exhibit movement toward a PD-like transcriptional state. Methods: Peripheral blood RNA-seq data were analyzed using harmonized metadata, DESeq2 differential-expression analysis, internally validated machine-learning models, PD-like projection, and integrated biomarker-panel prioritization. Independent baseline samples were used for cross-sectional differential-expression and machine-learning analyses. iRBD follow-up and post-conversion observations were excluded from baseline model development and reserved for exploratory longitudinal analyses. Results: Baseline analyses included 71 independent samples: 20 HC, 31 iRBD, and 20 PD. An additional 19 iRBD follow-up observations, including three post-conversion observations, were available for exploratory analyses. Differential-expression analysis identified 170 FDR-significant genes in PD versus HC and 85 in PD versus iRBD, compared with one FDR-significant gene in iRBD versus HC. Internal machine-learning validation showed stronger discrimination of manifest PD, with a best ROC-AUC of 0.883 for HC versus PD and 0.889 for iRBD versus PD. Discrimination between HC and iRBD was weak, with a best ROC-AUC of 0.584. PD-like projection scores were lowest in HC, highest in PD, and heterogeneous among baseline iRBD samples. Follow-up iRBD samples showed an exploratory upward shift in the mean PD-like projection score. Integrated prioritization produced a 24-gene PD candidate panel and a 24-gene exploratory iRBD panel, with genes in each panel supported by machine-learning feature-stability evidence and differential expression analysis. Conclusions: Manifest PD was associated with a distinct peripheral blood transcriptional signature, whereas iRBD-associated alterations were substantially weaker and more heterogeneous. The prioritized panels represent candidates for independent technical and external validation and should not yet be interpreted as clinically validated diagnostic or prognostic tests.

Isolated rapid eye movement sleep behavior disorder (iRBD) is characterized by nigro-striatal deficits, comprising dopaminergic denervation of the striatum and loss of dopaminergic cells in the substantia nigra (SN), that may herald phenoconversion to clinically manifest synucleinopathy. While phenoconversion has repeatedly been shown to relate to pre-synaptic dopaminergic deficits in the striatum, potential involvement of loss of dopaminergic cells in the SN remain unclear. In addition, phenoconversion may independently relate to noradrenergic deficits, stemming from cell loss in the locus coeruleus/subcoeruleus (LC/LsC) complex. Fifty-six iRBD patients were included and clinically followed over an 11-years as part of the ICEBERG study. Putamen dopamine denervation was quantified using 123I-FP-CIT single-photon emission computed tomography. Cell loss in the SN and LC/LsC was quantified using neuromelanin-sensitive magnetic resonance imaging (MRI). SN cell loss was additionally characterized as free water, derived from diffusion-weighted MRI. The primary outcome was time to phenoconversion. Cox proportional hazards regression was used to investigate relationships between phenoconversion risk and imaging predictors, estimated as hazard ratios (HRs). Out of 56 patients, 24 (41%) converted to a clinically manifest synucleinopathy [PD=14 (58%), DLB=8 (33%), MSA=2 (8%)] over a maximum period of 11 years. We replicated the well-established finding that reduced putamen DaT confers an increased phenoconversion risk [HR (95%CI)=3.1 (1.7-5.5), P<0.001]. We extend on this by showing a similar relationship for SN neuromelanin [HR (95%CI)=2.5 [1.3-4.6], P=0.004], SN free water [HR (95%CI)=1.54 (1.06-2.24), P=0.025], and LC/LsC neuromelanin [HR (95%CI)=2.1 (1.2-3.7), P=0.011], demonstrating involvement of the broader nigro-striatal dopaminergic system along with potential involvement of noradrenergic neurotransmission. When adjusting for putamen DaT, the relationship between phenoconversion risk and SN neuromelanin was attenuated [P=0.16], suggesting partial overlap between the metrics. In contrast, when modelled together, SN neuromelanin [HR (95%CI)=2.8 (1.4-5.6), P=0.003] and LC/LsC neuromelanin [HR (95%CI)=2.3 (1.1-4.8), P=0.037] contributed to phenoconversion risk independently of each other, indicating a differential contribution of dopaminergic and noradrenergic neurotransmitter deficits to iRBD phenoconversion. We demonstrate that phenoconversion in iRBD relates similarly to dopaminergic denervation of the putamen and cell loss in the SN. This opens possibilities for using NM-MRI, which can simultaneously capture dopaminergic and noradrenergic deficits, as an alternative to nuclear imaging techniques when estimating phenoconversion risk in iRBD.

Parkinson's disease (PD) exhibits pronounced sex differences, yet the underlying genetic and molecular mechanisms remain poorly understood. We performed the largest-to-date meta-analysis of sex-stratified genome-wide association studies of PD followed by brain proteogenomics-based causal inference analyses. We nominated 10 candidate proteins that appear important to sex-biased PD risk, of which 2 female-biased, GALC and PSMG1, and 3 male-biased, ACTR1B, WDR41, and CD151, were most robustly prioritized. Together, our findings provide evidence for genetic sex differences in PD, prioritizing sex-biased proteins implicated in lysosomal regulation, neuroinflammation, lipid biology, and other PD-relevant mechanisms, and highlighting potential sex-informed therapeutic opportunities.

Pathogenic missense variants in the X chromosome gene UBQLN2 cause amyotrophic lateral sclerosis (ALS), often accompanied by frontotemporal dementia (FTD). As an X-linked gene, UBQLN2 is subject to X chromosome inactivation (XCI), a process wherein one X chromosome in each cell is randomly inactivated to a Barr body throughout the body in females, creating a mosaic of allelic expression in the tissues of heterozygotes. Despite heterozygous females constituting a majority of reported cases of UBQLN2-linked ALS/FTD, and the known influence of XCI on neurological disorders at large, no current disease models account for XCI. Here we report the characterisation of 12 iPSC clones carrying the ALS/FTD-causing p.T487I (c.1460C>T) UBQLN2 variant. These clones, originally derived from 3 heterozygous carrier fibroblast lines, underwent validation of homeostatic Barr body retention. Erosion of XCI in a subset of the lines was correlated with biallelic expression (of both wildtype and mutant UBQLN2), as measured through a novel allele-selective qPCR (AS-qPCR) assay and verified by amplicon-based Illumina sequencing and Sanger chromatogram quantification, enabling selection of iPSC clones best retaining XCI. Together, this UBQLN2 AS-qPCR assay and selected iPSC clones will enable studies of the role of XCI and its skew in female resilience to UBQLN2 p.T487I-linked ALS/FTD and enable development of allele-selective therapies.

Cognitive impairment is a disabling feature of Long COVID with no established disease-modifying therapy, and little is known about the biological changes accompanying clinical improvement. Microtesla Magnetic Therapy (MMT) is a low amplitude radiofrequency electromagnetic field intervention delivered to the whole brain. In a randomized, sham-controlled feasibility trial, at home MMT was feasible, safe, and well tolerated, with evidence of clinical improvement among treated participants. We explored molecular changes associated with response using SomaScan 11K plasma proteomics on paired baseline and week 4 samples. Participants were classified post hoc within each treatment arm using a clinician-selected response phenotype integrating cognitive and symptom domains. These groups were used for proteomic, pathway, and OrganAge analyses. MMT response was associated with selective proteome remodeling and an exploratory 17 protein response pattern in which Hedgehog interacting protein (HHIP), a Hedgehog signaling antagonist, was most strongly associated with response. Directional pathway analysis identified patterns consistent with lower inflammatory and injury biology and higher repair and adaptive remodeling. OrganAge analysis showed trends toward lower Brain and Organismal OrganAge with MMT. These findings prioritize HHIP and the exploratory 17 protein response pattern for prospective validation and support evaluation of plasma proteomics for monitoring treatment response.

INTRODUCTION: Parkinson's disease (PD) presents with motor and non-motor symptoms, including dementia, but the severity and rate of cognitive decline are heterogeneous and difficult to predict clinically. METHODS: We quantified baseline serum proteins with the high-throughput SomaScan(R) assay in 834 PD individuals and performed Cox regression to identify proteins associated with subsequent development of dementia. Candidate biomarker proteins were replicated in 371 individuals from an independent cohort and meta-analysed. RESULTS: Protein targets significantly associated with progression to dementia were predominantly involved in synaptic plasticity, protein degradation/lysosomal function and extracellular matrix organisation. Mendelian Randomisation further revealed that changes in the Nogo receptor RTN4R may be causally associated with the development of Lewy body dementia. DISCUSSION: We identified several proteins predicting progression to dementia in PD, indicating changes in blood proteome that precede the development of clinical symptoms by several years, providing a window of opportunity to identify at-risk individuals early on.

Adapting ongoing gait patterns to environmental challenges is essential for safe navigation through the environment. Impairment of gait adaptation is common in many neurodegenerative disorders, such as Parkinson's disease (PD), where it hampers mobility and limits quality of life. The neural control of gait adaptation remains largely unclear, thereby limiting the development of targeted treatments, such as deep brain stimulation of the subthalamic nucleus (STN-DBS). We integrated clinical, kinematic, brain metabolic imaging, and electrophysiological data, obtained during a fully immersive virtual reality overground walking task, to characterize the neural underpinnings of gait adaptation performance during dynamic obstacle avoidance and its improvement with STN-DBS. Movement kinematics, brain oscillatory activity, and metabolic activation were simultaneously acquired in 12 patients with PD during rest and gait adaptation, under active or paused STN-DBS, using inertial measurement units, electroencephalography, and three separate [18F]fluorodeoxyglucose positron emission tomography scans. Eight age-matched healthy subjects completed the same task for comparative kinematic analyses. All patients showed significant clinical improvement with STN-DBS. During the gait adaptation task with paused stimulation, patients exhibited increased metabolic activity in the cerebellum and sensorimotor cortex. Active STN-DBS selectively enhanced thalamic and superior frontal gyrus (SFG) metabolism, while concomitantly reducing cerebellar uptake. Right-lateralized SFG metabolism correlated with gait adaptation performance, with DBS-driven shifts toward greater right SFG activity predicting the magnitude of gait adaptation improvement. This correlation was independent of baseline asymmetry in clinical impairment, electrode placement, or structural connectivity to the SFG. Of note, STN-DBS amplitude asymmetry emerged as an independent predictor of right-lateralization of SFG metabolism. EEG recordings confirmed this lateralized network modulation, with theta-band asymmetry paralleling PET findings. Our findings identify a lateralized thalamo-cortical network supporting gait adaptation in PD and highlight a distinctive role for the SFG. We further show that effective STN-DBS acts as a lateralized regulator, dynamically rebalancing cortico-thalamic circuits to support context-appropriate gait control. The observed right-hemispheric lateralization may foster novel image-guided programming strategies to enhance the consistency and effectiveness of gait control in PD.

Mild malformation of cortical development with oligodendroglial hyperplasia and epilepsy (MOGHE) is a recently recognized cause of drug-resistant focal epilepsy. It is often MRI-negative or shows imaging features mimicking focal cortical dysplasias, which makes recognition difficult and limits presurgical counseling. We aimed to identify an intracranial EEG (iEEG) biomarker that distinguishes MOGHE from other developmental brain lesions encountered in epilepsy surgery. In a retrospective multicenter test cohort of 38 patients (18 MOGHE, 20 non-MOGHE), we analyzed long-term stereo-EEG and subdural recordings. Only MOGHE patients showed highly stereotyped clusters of very brief low-voltage fast activity (LVFA) events, organized into status-like 3 to 12-minute episodes that often lacked clear clinical symptoms. LVFA clusters were present in 16/18 MOGHE and 0/22 non-MOGHE patients. We then tested diagnostic performance in an independent, blinded single-center validation cohort of 22 patients (11 MOGHE, 11 non-MOGHE), in which visual identification of LVFA clusters correctly classified 10/11 MOGHE and 10/11 non-MOGHE cases (Cohens kappa=0.82). Penalized logistic regression further confirmed MOGHE histology as the strongest predictor of LVFA clusters, independent of age and lobe localization. Because LVFA clusters can be recognized visually on routine intracranial EEG recordings without specialized software, this biomarker is readily applicable in clinical practice and may improve presurgical identification of MOGHE. Future prospective studies should determine whether its recognition influences surgical planning, improves outcome prediction, or facilitates selection of patients for mechanism-based therapies.

Abstract Background: Deep Brain Stimulation (DBS) surgery is a treatment of choice for movement disorders, and utilizes an implanted electrical pulse generator that administers electrical stimulation to designated brain regions responsible for motor control. The preoperative identification of effective predictive factors is of utmost importance for appropriate patient selection. In this study, we evaluate the potential of machine learning-based neuroimaging for predicting DBS outcomes. (PROSPERO Registration: CRD420261279318) Method: Following the PRISMA statement, eligible studies were selected through searching three databases (PubMed, Scopus, Web of Science) on November 6, 2025. Methodological quality was assessed using the PROBAST+AI tool. Random-effects models pooled discrimination performance (AUC). Heterogeneity was investigated using meta-regressions for age and gender alongside subgroup analysis by type of algorithm. Publication bias was assessed using Egger regression test. Results: Twenty studies were included in the analysis. Most investigations focused on PD, STN-DBS, and postoperative motor improvement, while a smaller number assessed neuropsychiatric outcomes. Overall, the pooled discrimination for models predicting motor outcomes showed an AUC of 0.86, and the pooled models for delirium showed an AUC of 0.87. Regarding the risk of bias assessment, seven studies were classified as low risk, while thirteen were identified as high risk. Conclusion: Machine learning-based neuroimaging shows promising potential for preoperative prediction of DBS outcomes. However, the current literature is characterized by a persistent gap between encouraging discrimination and reliable clinical readiness. The main weakness of the field lies in analytical rigor and generalizability. These models should currently only be considered as promising research tools.

Introduction: Parkinson's disease is a neurodegenerative disease affecting motor and cognitive function that has a major impact on society. Epidemiological evidence has suggested that the incidence of PD may be increasing; however, the underlying etiology is unclear. Here, we investigated the role of increasingly used antipsychotics in the diagnosis of PD. Methods: We harnessed Merative Marketscan insurance claims databases to conduct a case-control study of 65,275 new cases of PD and 652,364 age-, sex-, and time-matched controls. We estimated associations between exposure and duration of use for antipsychotics adjusted for important confounders using fixed effects logistic regression. We performed sensitivity analyses stratified by the level of D2 receptor inhibition to assess dose-response relationships; a lagged exposure analysis to address confounding by indication; analysis assessing exposure of other psychiatric medications without significant D2 inhibition (bupropion, trazodone, and Z-drugs); analysis assessing exposure of non-psychiatric medications with significant (metoclopramide) or no D2 inhibition (ondansetron). Results: We found cases with PD had elevated odds of antipsychotic exposure. Longer durations of exposure and greater affinity for the D2 receptor were associated with greater associations with PD. There was a dose-response relationship between D2 inhibition activity and increased odds of PD for a similar duration of exposure. There was a dose response relationship between duration of metoclopramide and the odds of PD; however, there was no such relationship between the non-D2 inhibiting control medications. The association between exposure to an antipsychotic and increased odds of PD was present even when the first exposure was 10 years prior to the PD diagnosis date. Conclusion: If these results are causal, antipsychotic use may explain up to 2.4% of all cases of PD. Given the increasing rate of use of these medications, and the concurrent increasing age-adjusted incidence of PD, there is an urgent need for further investigation into this association and greater awareness of the potential risks of these medications in older adults.